Emergency Medicine

[Development of a planned and monitoring robotic assistance and automation for application in orthopedics and trauma surgery].

Robot-assisted surgery offers many advantages with respect to precision and facilitation in medicine, whereby the physician controls the system externally by guiding the movement of the robot during the operation. Despite training and experience, operating errors by the user cannot be excluded. In addition, for the established systems the precise guidance of instruments along complexly shaped surf...

Artificial intelligence and machine learning for hemorrhagic trauma care.

Artificial intelligence (AI), a branch of machine learning (ML) has been increasingly employed in the research of trauma in various aspects. Hemorrhage is the most common cause of trauma-related death. To better elucidate the current role of AI and contribute to future development of ML in trauma care, we conducted a review focused on the use of ML in the diagnosis or treatment strategy of traumat...

Robot-Assisted Resection of Left Ventricular Papillary Fibroelastoma Arising From the Mitral Chordal Apparatus.

The application of robot-assisted thoracoscopy to cardiac surgery affords an opportunity to leverage the exceptional intraoperative exposure, visualiz...

Spatial and deep learning analyses of urban recovery from the impacts of COVID-19.

This study investigates urban recovery from the COVID-19 pandemic by focusing on three main types of working, commercial, and night-life activities an...

Modeling acute care utilization: practical implications for insomnia patients.

Machine learning models can help improve health care services. However, they need to be practical to gain wide-adoption. In this study, we investigate...

Conventional Versus Artificial Intelligence-Assisted Interpretation of Chest Radiographs in Patients With Acute Respiratory Symptoms in Emergency Department: A Pragmatic Randomized Clinical Trial.

OBJECTIVE: It is unknown whether artificial intelligence-based computer-aided detection (AI-CAD) can enhance the accuracy of chest radiograph (CR) int...

User Feedback on the Use of a Natural Language Processing Application to Screen for Suicide Risk in the Emergency Department.

Suicide is the 10th leading cause of death in the USA and globally. Despite decades of research, the ability to predict who will die by suicide is sti...

The role of Artificial intelligence in the assessment of the spine and spinal cord.

Artificial intelligence (AI) application development is underway in all areas of radiology where many promising tools are focused on the spine and spi...

Machine learning to improve frequent emergency department use prediction: a retrospective cohort study.

Frequent emergency department use is associated with many adverse events, such as increased risk for hospitalization and mortality. Frequent users hav...

Development and evaluation of deep learning algorithms for assessment of acute burns and the need for surgery.

Assessment of burn extent and depth are critical and require very specialized diagnosis. Automated image-based algorithms could assist in performing w...

Deep learning-based hemorrhage detection for diabetic retinopathy screening.

Diabetic retinopathy is a retinal compilation that causes visual impairment. Hemorrhage is one of the pathological symptoms of diabetic retinopathy th...

Improved neural network for predicting blood donations based on two emergent factors.

BACKGROUND: Blood donation forecasting is a critical part of blood supply chain management. However, few studies have focused on modeling blood donati...

XAIRE: An ensemble-based methodology for determining the relative importance of variables in regression tasks. Application to a hospital emergency department.

Nowadays it is increasingly important in many applications to understand how different factors influence a variable of interest in a predictive modeli...

Benchmarking of Machine Learning classifiers on plasma proteomic for COVID-19 severity prediction through interpretable artificial intelligence.

The SARS-CoV-2 pandemic highlighted the need for software tools that could facilitate patient triage regarding potential disease severity or even deat...

Deep Learning Analysis of Chest Radiographs to Triage Patients with Acute Chest Pain Syndrome.

Background Patients presenting to the emergency department (ED) with acute chest pain (ACP) syndrome undergo additional testing to exclude acute coron...

Application of robot-assisted endoscopic technique in the treatment of patent ductus arteriosus in 106 children.

The objective is to evaluate and apply the robot-assisted endoscopic surgical technique for treatment of patent ductus arteriosus (PDA) in children. C...

Development and External Validation of a Deep Learning Algorithm to Identify and Localize Subarachnoid Hemorrhage on CT Scans.

BACKGROUND AND OBJECTIVES: In medical imaging, a limited number of trained deep learning algorithms have been externally validated and released public...

Autonomous Driving Control Based on the Technique of Semantic Segmentation.

Advanced Driver Assistance Systems (ADAS) are only applied to relatively simple scenarios, such as highways. If there is an emergency while driving, t...

Machine Learning as an Adjunct to Traditional Triage in the Emergency Department.

Transfer learning-based ensemble convolutional neural network for accelerated diagnosis of foot fractures.

The complex shape of the foot, consisting of 26 bones, variable ligaments, tendons, and muscles leads to misdiagnosis of foot fractures. Despite the i...